Predicting Blood-Brain Permeation from Three-Dimensional Molecular Structure Patrizia Crivori, Gabriele Cruciani, Pierre-Alain Carrupt, and Bernard Testa.

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Predicting Blood-Brain Permeation from Three-Dimensional Molecular Structure Patrizia Crivori, Gabriele Cruciani, Pierre-Alain Carrupt, and Bernard Testa. J. Med. Chem : Presented by Ankit Garg

To cross or not to cross?  Some drugs must cross the blood-brain barrier (BBB), others absolutely should not.  Experimentally screening for BBB permeability is expensive.

Many factors influence BBB permeability  H-bonding capacity  Hydrophobicity  Ionization profile  Molecular size  Lipophilicity  Flexibility  Plasma protein binding  Active efflux from CNS  Metabolism

The VolSurf difference  Past approaches have emphasized one or a few factors, without regard to the rest:  Lipophilicity  Solvatochromatic parameters  Topological indices  VolSurf uses 72 descriptors derived from 3D molecular interaction fields.

Multivariate Analysis: PCA  Represent multivariate data along a few, principal component axes. Multivariate Data Output Plots

Two Sets of Data:  “Training” set was based on 44 compounds used in prior work.  “External Prediction” set was based on 108 wide-ranging drugs from literature.  Compounds in both sets had well- characterized BBB behavior.

Choosing Descriptors  VolSurf descriptors are obtained directly from 3D molecular interaction fields. Not sure what this means.  Main difference appears to be that VolSurf descriptors have a clear chemical meaning.

Encouraging 1 st Results! PCA on the training set

PCA on BBB+ compounds of the second data set 90% Accuracy! (40 out of 44)

PCA on BBB- compounds of the second data set 65% Accuracy! (46 out of 71)

Reasons for differences in accuracy  Many BBB- compounds passively defuse into the brain but:  are then metabolized before acting  are actively effluxed  At least one compound, Mequitazine, is likely misclassified in the literature based on the results of this study and another one as well.

PLS discriminant analysis: Finding two latent variables Unlike PCA, relies on training, so descriptors can be differentially biased >90% accurate, though with a confidence interval

Most important descriptors Though some descriptors are clearly more important, in general a balance of many descriptors control BBB permeability

Questions:  Paper notes that it is impossible to model mixtures of stereoisomers in 3D? Why is this the case?  To account for conformational variety in the tested molecules, the authors used two different programs to generate low-energy conformations for the same molecule. The data they then generated for these pair complements was similar, suggesting that the conformational differences mattered little. Is this analysis convincing?  What are the relative strengths and weaknesses of using PCA vs. PLS discriminant analysis?